• Human genome is big - ~3.2 billion base pairs (~6ft long)
• 20,000-25,000 genes (~2% of the genome)

• Human genome contains ~4M single nucleotide polymorphisms (SNPs)

• No new SNPs - discovery of new SNPs appears to be saturated at ~8,500 deep sequenced genomes (Telenti A. et.al. "Deep sequencing of 10,000 human genomes" PNAS 2016 http://www.pnas.org/content/113/42/11901)

• DNaseI hypersensitive sites
• Histone modification marks
• Transcription Factor Binding Sites
• DNA methylation
• Enhancers
• …

Collectively referred hereafter as epigenomic or regulatory regions

Epigenomic data = genome annotation data = regions other than DNA sequence, annotated as carrying functional/regulatory potential or having a biological property

• Genome is not linear - the spatial organization plays an important role in regulation of gene expression
• Chromosome Conformation Capture sequencing technologies allow exploring long-range interactions and loop- and domain formation

• Long-range interactions compartmentalize a variety of regulatory elements to regulate gene expression
• Disruption of one or several of such interactions => gene expression changes => disease

There is a growing gap between the generation of massively parallel sequencing output and the ability to process and analyze the resulting data. New users are left to navigate a bewildering maze of base calling, alignment, assembly and analysis tools with often incomplete documentation and no idea how to compare and validate their outputs. Bridging this gap is essential, or the coveted $1,000 genome will come with a $20,000 analysis price tag.

Enrichment analysis – detection whether a group of objects has certain properties more (or less) frequent than can be expected by chance

• Gene set enrichment analysis - summarizing many genes of interest, such as differentially expressed genes, with a few common gene annotations (molecular functions, canonical pathways)

• Epigenomic enrichment analysis - summarizing many genomic regions of interest, such as disease-associated genomic variants, with a few common genome annotations (chromatin states, transcription factor binding sites)

• Hypothesis: SNPs in epigenomic regions may disrupt regulation
• More significant enrichment => more SNPs in epigenomic regions => more regulation is disrupted (SNP burden)

• Epigenomic elements enriched in SNPs (altered by them) may be the new therapeutic target

• Systems biology approach - don't try to fix one faulty SNP, target the whole regulatory system affected by all SNPs

Experimental data vs. epigenomic data

• Differentially expressed genes, promoters
• Differentially methylated regions
• SNPs, copy number variations, Insertions/Deletions, Structural Variants
• ChIP-seq peaks
• 3D interacting regions
• …

• 6 out of 7 disease-associated SNPs overlap with epigenomic marks
• How likely this to be observed by chance? (Hypergeometric test/Binomial test/Permutation test)

Hypergeometric test

• \(m\) is the total number of SNPs
• \(j\) is the number of SNPs annotated with a property
• \(n\) is the number of selected SNPs
• \(k\) is the number of selected SNPs annotated with a property

Hypergeometric test

• \(m\) is the total number of SNPs
• \(j\) is the number of SNPs annotated with a property
• \(n\) is the number of selected SNPs
• \(k\) is the number of selected SNPs annotated with a property

Hypergeometric test

• \(m\) is the total number of SNPs
• \(j\) is the number of SNPs annotated with a property
• \(n\) is the number of selected SNPs
• \(k\) is the number of selected SNPs annotated with a property

What is the probability of having \(k\) or more annotated SNPs among the selected \(n\) SNPs?

\[P = \sum_{i=k}^n{ \frac{{m-j \choose n-i}{j \choose i}}{{m \choose n}} }\]

• Hypothesis - Genomic variants associated with different diseases may be enriched in (= disrupt) similar regulatory elements.

Different diseases - similar mechanisms

• Epigenomic similarity among diseases - similar treatment strategies

• Unknown genetic disorders can be matched to known diseases by epigenomic similarity

• Patients can be matched by epigenomic similarity of their genomes

39 SNP sets associated with complex diseases and traits:

• 21 immunologic diseases
• 4 neurologic diseases/traits
• 7 metabolic diseases/traits
• 7 other traits

Epigenomic enrichment profile – SNP set-specific vector of epigenomic enrichment (\(-log_{10}\)-transformed) p-values

\(-log_{10}\)-transformed epigenomic enrichment profiles can be compared using Spearman correlation, PCA

• Better understanding of the regulatory mechanisms associated with subgroups of diseases

• Finer stratification of patients by the epigenomic differences associated with their genomes

• More precise treatment strategies

• Define two groups of SNP sets (use epigenomic similarity analysis)
• We are testing whether the level of enrichment differs between groups of SNP sets

Quiescent chromatin states, T helper cell-specific - enriched in disease SNPs in red cluster

Enhancers, T helper cell-specific - enriched in disease SNPs in green cluster

• Epigenomic elements are cell- and tissue type specific

• Hypothesis - a SNP set is more likely to be enriched in epigenomic elements from cell types relevant to phenotype

• Cell type-specific epigenomic enrichment analysis identifies cell types where enrichments are the most significant

• More focused understanding of functional impact caused by SNPs

• Treatment of epigenomic abnormalities in relevant cell types

Detection of cell/tissue types with many regulatory marks enriched in SNPs

• Global distribution of enrichments - the null model
• Cell type-specific distribution of enrichments - the alternative model
• Test whether cell type-specific enrichments are significantly higher than the global level of enrichments

• Alzheimer SNPs are enriched in brain-specific epigenomic marks

• 431 patients diagnosed with Systemic Lupus Erythematosus (SLE)

• Exome sequencing - SNP sets

• Focus on rare variants - patient-specific SNP sets

• There is ~4 times more rare SNPs (MAF<5%) than common SNPs (MAF >5%)

• Common SNPs are largely the same in healthy controls and SLE patients

• ~50% of rare SNPs are specific for either healthy controls, or SLE patients

• Patients may be diagnosed with the same disease, but have different underlying mechanisms

• Patient-specific SNP sets may have similar epigenomic enrichment profiles

• Patients can be classified by similarity of their epigenomic enrichment profiles

• Subgroups of patients having differential epigenomic enrichments may have different clinical outcomes

• GenomeRunner defines potential functional impact of SNP sets via epigenomic enrichment analysis

• Epigenomic similarity analysis identifies regulatory similarity and differences among SNP sets

• Cell type-specific enrichment analysis prioritizes cell/tissue type specificity of the epigenomic enrichments

• Epigenomic enrichment analyses can be applied to any genomic signature, from disease-associated SNP sets to patient-specific genotypes

• Algorithms for processing various genomic data formats into a unified format amenable for fast statistical analysis (https://github.com/ryanlayer/giggle)

3D interaction data

• Loops
• Topologically associated domains
• Stable/unstable 3D genomic regions
• Strongly interacting regions

• Busby, George (2016), “Genotype data for a set of 163 worldwide populations”, https://data.mendeley.com/datasets/ckz9mtgrjj/1
• 2,643 individuals from 163 worldwide human populations.
• Epigenomic and 3D similarities and differences in population genome architecture

• GRASP: Genome-Wide Repository of Associations Between SNPs and Phenotypes
• 2,082 GWAS studies.
• Epigenomic and 3D similarities and differences in genomic architecture of complex diseases

• TCGA - 11,000 patients, 33 tumor types, 7 data types (genotypes, gene expression, methylation etc.).
• Epigenomic classification of cancer subtypes
• Survival differences associated with patient-specific epigenomic signatures

• Jonathan Wren, Oklahoma Medical Research Foundation
• Cory Giles, Oklahoma Medical Research Foundation
• Lukas Cara, University of St. Thomas, Houston
• Jianlin Cheng, University of Missouri, Columbia
• Tuan Trieu, University of Missouri, Columbia
• Bridget Thomson-McInnes, Virginia Commonwealth Univrsity
• John Stansfield, Virginia Commonwealth Univrsity
• Kellen Cresswell, Virginia Commonwealth Univrsity